Last AI-900 practice test reviews: Practice Test Microsoft dumps
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Preparing for the AI-900 exam is relatively easy, and Microsoft provides a range of learning resources to help you prepare for the exam, including online courses, study guides, and practice exams. AI-900 exam is available in several languages and is accessible online, making it convenient for individuals to take the exam from anywhere in the world.
For more info read reference:
Microsoft AI-900 Official Certification Learning Site
Training and Certification Guide
Microsoft AI-900 certification exam is an ideal starting point for individuals who want to build a career in AI and ML. Microsoft Azure AI Fundamentals certification exam is designed to test your foundational knowledge of AI and ML concepts and how they can be applied to the Azure platform. By passing the AI-900 exam, you will gain a better understanding of how AI and ML can be used to solve real-world problems, and you will be able to use Azure AI services and tools to build intelligent applications.
NEW QUESTION # 64
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks
NEW QUESTION # 65
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://machinelearningmastery.com/difference-test-validation-datasets/
NEW QUESTION # 66
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features Speech recognition means Speech to Text. In the above example as a person speaks the words are converted into text of the same language. Hence Speech to Text also called Speech recognition is the right answer.
Speech recognition - the ability to detect and interpret spoken input.
Speech synthesis - the ability to generate spoken output.
https://docs.microsoft.com/en-us/learn/modules/recognize-synthesize-speech/1-introduction
NEW QUESTION # 67
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-designer-python
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml
NEW QUESTION # 68
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 69
Match the facial recognition tasks to the appropriate questions.
To answer, drag the appropriate task from the column on the left to its question on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/face/#features
NEW QUESTION # 70
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
NEW QUESTION # 71
You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 72
You send an image to a Computer Vision API and receive back the annotated image shown in the exhibit.
Which type of computer vision was used?
- A. image classification
- B. semantic segmentation
- C. object detection
- D. optical character recognition (OCR)
Answer: C
Explanation:
Explanation
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like
"indoor", which can't be localized with bounding boxes.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION # 73
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Yes
Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review.
The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review.
Box 2: No
Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Box 3: Yes
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Reference:
https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 74
For each of the following statements, select Yes if the statement is True. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 75
Match the tool to the Azure Machine Learning task.
To answer, drag the appropriate tool from the column on the left to its tasks on the right. Each tool may be used once, more than once, or not at all NOTE: Each correct match is worth one point.
Answer:
Explanation:
NEW QUESTION # 76
You have the following dataset.
You plan to use the dataset to train a model that will predict the house price categories of houses.
What are Household Income and House Price Category? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/interpret-model-results
NEW QUESTION # 77
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://developers.google.com/machine-learning/practica/image-classification
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-model-builder
https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/
NEW QUESTION # 78
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Yes
Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review.
The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review.
Box 2: No
Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Box 3: Yes
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Reference:
https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 79
You send an image to a Computer Vision API and receive back the annotated image shown in the exhibit.
Which type of computer vision was used?
- A. image classification
- B. semantic segmentation
- C. object detection
- D. optical character recognition (OCR)
Answer: C
Explanation:
Section: Describe features of computer vision workloads on Azure
Explanation:
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION # 80
You plan to deploy an Azure Machine Learning model by using the Machine Learning designer Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
1 - Ingest and prepare a dataset.
2 - Split the datga randomly into training data and validation data
3 - Tain the model.
4 - Evaluate the model against the validation dataset.
NEW QUESTION # 81
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-science-process/create-features
NEW QUESTION # 82
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